Presentation - National Water Research Institute

Characterization of DOM in humic rich tributaries of a
drinking water reservoir in Saxony using
FTICR/MS and EEMF

Helmholtz Centre
For Environmental Research – UFZ
Peter Herzsprung, W. v. Tümpling, J. Bravidor
(peter.herzsprung@ufz.de)
Helmholtz Zentrum München
German Research Centre for Environmental Health
N. Hertkorn, M. Harir, P. Schmitt-Kopplin
Characterization of DOM in humic rich tributaries of a
drinking water reservoir in Saxony using
FTICR/MS and EEMF

Problem: In Saxony drinking water is supplied
from Ore Mountains reservoirs influenced
by increasing DOC concentration of raw water

Problem: In Saxony drinking water is supplied
from Ore Mountains reservoirs influenced
by increasing DOC concentration of raw water
Consequence:
Usability of reservoir water as raw water can be limited by:
- unpleasant taste, odour, and colour
- increase of flocculation costs
- undesirable harmful disinfection byproducts

Questions to be answered.
1. What can be said about the relation between
fluorescence and DOC?
2. Is there any seasonal influence on the fluorescence
DOC relation?
3. What can be said about the abundance of typical
compound groups?
4. How can the information be used for using best
raw drinking water?

water sampling
DOC
UV
EEMF
FTICR/MS
bulk
concentration
bulk
optical
properties
group
species
elemental
formulae
data analysis
biogeochemical evaluation

water sampling
DOC
UV
EEMF
FTICR/MS
bulk
concentration
bulk
optical
properties
group
species
elemental
formulae
data analysis
biogeochemical evaluation

Catchment area of reservoir Muldenberg
Effluent TSP
Saubach
SBA
Red Mulde
RMU
White Mulde
WMU
Sauteich
STE
Germany
Saxony
Muldenberg
1 km
W
N
S
E

Reservoir
Effluent
Coloured water

water sampling
DOC
UV
EEMF
FTICR/MS
bulk
concentration
bulk
optical
properties
group
species
elemental
formulae
data analysis
biogeochemical evaluation

water sampling
DOC
UV
EEMF
FTICR/MS
bulk
concentration
bulk
optical
properties
group
species
elemental
formulae
data analysis
biogeochemical evaluation

methods
2 0 0 0
1 8 0 0
1 6 0 0
1 4 0 0
1 2 0 0
1 0 0 0
8 0 0
6 0 0
4 0 0
2 0 0
0
3D fluorescence (EEMF)
2 4 0
2 5 0
2 6 0
2 7 0
2 8 0
2 9 0
3 0 0
3 1 0
3 2 0
3 3 0
3 4 0
3 5 0
3 6 0
intensity
(A.U.)
emission 260 – 585 nm
excitation
240 – 360 nm

methods
humic-like
Ligninphenol
like
HO
H
C
R
?
protein-like
Excitation (nm)
360
340
320
300
0
100
200
300
400
500
Intensity
(A.U.)
HO
OCH 3
280
According to Burdige et al,
Marine Chem. 51, 325-346 (2004)
260
240
Contour plot
300 350 400 450 500 550
Emission (nm)

methods
Scaling of fluorescence diagrams
Excitation (nm)
Seite 14
360
340
320
300
280
260
240
Sampling site, date
0
200
400
600
800
Intensity (A.U.)
300 350 400 450 500 550
Emission (nm)
Median values
Excitation:
320 – 345 nm
Emission:
410 – 440 nm
Wave length area
for evaluation of
fluorescence intensity
(Humic like fluorescence)

water sampling
DOC
UV
EEMF
FTICR/MS
bulk
concentration
bulk
optical
properties
group
species
elemental
formulae
data analysis
biogeochemical evaluation

OH
O
H
H
O
C
OH
H
H
HOOC
OH
H
O
C
C
COOH
H
HO
H
H
C 17 H 14 O 11 - millions of isomeric solutions?
HO
HO
OH
O
OH
H
HO
HO
OH
OH
OH
O C C
H
H
H
H

methods
FTICR/MS elemental formulae
van Krevelen diagrams: geochemical pools of DOM
2,5
2.5
Simplified illustration
H/C ratio
2.0 2
1.5 1,5
1.0
1
0.5
0,5
lipids
lignins
black carbon
proteins
carbohydrates
tannins
0 0 0.2 0.4 0.6 0.8 1.0
0 0,2 0,4 0,6 0,8 1
O/C ratio

Questions to be answered.
1. What can be said about the relation between
fluorescence and DOC?
2. Is there any seasonal influence on the fluorescence
DOC relation?
3. What can be said about the abundance of typical
compound groups?
4. How can the information be used for using best
raw drinking water?

esults
Humic like fluorescence versus DOC
March July August
RMU 0309
RMU 0709
RMU 0809
RMU
WMU
excitation
Excitation (nm)
Excitation (nm)
360
340
320
300
280
260
240
360
340
320
Excitation (nm)
300
440 A.U. 280
622 A.U.
WMU 0309
Excitation (nm)
360
340
320
260
240
260
0
200
400
600
800
Excitation (nm)
360
340
320
300
280
260
11.7 mg/l 10.3 mg/l
240
300 350 400 450 500 550
300 350 400 450 500 550
Emission (nm)
WMU 0709
300
300
435 A.U.
280
280 453 A.U.
260
240
0
200
400
600
800
9.1 mg/l
300 350 400 450 500 550
0
200
400
600
800
Emission (nm)
10.8 mg/l
300 350 400 450 500 550
Emission (nm)
360
340
320
0
200
400
600
800
8.2 mg/l
240
300 350 400 450 500 550
Emission (nm)
emission
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
577 A.U.
0
200
400
600
800
Emission (nm)
WMU 0809
347 A.U.
5.7 mg/l
300 350 400 450 500 550
Emission (nm)

Answer
- No direct relation between the fluorescence
and the DOC.
- EEMF generates new information

Questions to be answered.
1. What can be said about the relation between
fluorescence and DOC?
2. Is there any seasonal influence on the fluorescence
DOC relation?
3. What can be said about the abundance of typical
compound groups?
4. How can the information be used for using best
raw drinking water?

esults Humic like fluorescence at different seasons
excitation
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
January March April July August
RMU 0109
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
Excitation (nm)
Excitation (nm)
360
340
320
300
280
260
240
Emission (nm)
WMU 0109
360
0
200
400
340
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
SBA 0109
360
0
200
400
340
600
800
320
300
Excitation (nm)
280
260
240
300 350 400 450 500 550
Emission (nm)
STE 0109
360
0
200
400
340
600
800
320
300
Excitation (nm)
280
260
240
300 350 400 450 500 550
Emission (nm)
TSP 0109
RMU 0309
RMU 0409
360
0
0
200
200
400
340 400
600
600
800
800
320
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
300
RMU
280
260
240
300 350 400 450 500 550
300 350 400 450 500 550
Emission (nm)
Emission (nm)
WMU 0309
WMU 0409
360
0
0
200
200
400
340 400
600
600
800
800
320
300
WMU
280
260
240
300 350 400 450 500 550
300 350 400 450 500 550
Emission (nm)
Emission (nm)
SBA 0309
SBA 0409
360
0
0
200
200
400
340 400
600
600
800
800
320
300
SBA
280
260
240
300 350 400 450 500 550
300 350 400 450 500 550
Emission (nm)
Emission (nm)
STE 0309
STE 0409
360
0
0
200
200
400
340 400
600
600
800
800
320
300
STE
280
260
240
300 350 400 450 500 550
300 350 400 450 500 550
Emission (nm)
Emission (nm)
TSP 0309
TSP 0409
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
RMU 0709
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
WMU 0709
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
SBA 0709
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
STE 0709
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
TSP 0709
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
RMU 0809
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
WMU 0809
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
SBA 0809
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
STE 0809
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
TSP 0809
Excitation (nm)
Excitation (nm)
Excitation (nm)
Sept.
RMU 0909
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
WMU 0909
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
SBA 0909
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
TSP 0909
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
October December
RMU 1009
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
WMU 1009
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
SBA 1009
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
STE 1009
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
TSP 1009
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
RMU 1209
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
WMU 1209
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
SBA 1209
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
STE 1209
360
0
200
340 400
600
800
320
300
280
260
240
300 350 400 450 500 550
Emission (nm)
TSP 1209
pond tributaries
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
TSP
300 350 400 450 500 550
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
360
360
0
0
200
200
400
340 400
340
600
600
800
800
320
320
Excitation (nm)
300
300
280
280
260
260
240
240
300 350 400 450 500 550
300 350 400 450 500 550
emission
Emission (nm)
Emission (nm)
Excitation (nm)
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
effluent

esults
Seasonal behaviour of DOC and fluorescence
Quotient of
median fluorescence intensity
and DOC
Seite 23

Answer
- Specific fluorescence activity (of DOC) rise
between March and September.
- Specific fluorescence activity rise can not be explained
by EEMF.

Question to be answered.
1. What can be said about the relation between
fluorescence and DOC?
2. Is there any seasonal influence on the behaviour?
3. What can be said about the abundance of typical
compound groups?
- seasonal effect
- relation between fluorescence and
biogeochemical pools
4. How can the information be used for using best
raw drinking water?

esults
FTICR/MS data available
Quotient of
median fluorescence intensity
and DOC
Seite 26

esults
Bulk van Krevelen diagrams
2
1.5
1
0.5
0
black carbon
H/C
O/C
1
lipids
March
0 0.2 0.4 0.6 0.8 1
2
1.5
proteins carbohydrates
lignins
tannins
Evaluation of CHO formulae;
CHOS and CHON not considered
0.5
0
July
0 0.2 0.4 0.6 0.8 1

esults
Excitation (nm)
360
340
320
300
280
260
240
2
0
200
400
600
800
DOC
RMU3
RMU 0309
Difference in abundance of elemental formulae
Excitation (nm)
RMU 0709
280
260
9.1 mg/l 11.7 mg/l
300 350 400 450 500 550
Emission (nm)
360
340
320
300
240
0
200
400
600
800
DOC
RMU7
300 350 400 450 500 550
Emission (nm)
RMU3 + RMU7
2
1.5
1
0.5
0
2
RMU3
different abundance
0 0.2 0.4 0.6 0.8 1
RMU7
1.5
1.5
1
0.5
0
common abundance
H/C
0 0.2 0.4 0.6 0.8 1
1
0.5
O/C
0
different abundance
0 0.2 0.4 0.6 0.8 1

esults
mass peaks
biunique
assignment
intensity values
Rank analysis (different seasons) FTICR/MS
for 638 common elemental formulae in 4 samples
C 17 H 14 O 11
rank within
638 el.
formulas
rank within
4 samples
RMU3 124 4
March
RMU4 86 3
April
RMU7 36 1
July
RMU8 52 2
August

esults
mass peaks
Rank analysis (different seasons) FTICR/MS
for 638 common elemental formulae in 4 samples
2.5
biunique
assignment
C 17 H 14 O 11
rank within
638 el.
formulas
rank within
4 samples
RMU3 124 4
March
RMU4 86 3
April
RMU7 36 1
July
RMU8 52 2
August
intensity values
0 1
Van Krevelen diagrams
2
1.5
1
0.5
H/C
0
O/C
0
Rank 1
Rank 2
Rank 3
Rank 4
Red Mulde / July
depiction of the ranked formulae
0 0.2 0.4 0.6 0.8 1

esults
2
1.5
1
Rank analysis (different seasons) FTICR/MS
Red Mulde
2.5
0
Rank 1
Rank 2
Rank 3
Rank 4
0 1
lignins
tannins
0.5
0
March
H/C
O/C
0 0.2 0.4 0.6 0.8 1
2
1.5
Red Mulde
1
0.5
0
July
0 0.2 0.4 0.6 0.8 1

esults
2
1.5
1
Rank analysis (different seasons) FTICR/MS
Red Mulde
2.5
0
Rank 1
Rank 2
Rank 3
Rank 4
0 1
lignins
tannins
0.5
0
March
H/C
O/C
0 0.2 0.4 0.6 0.8 1
2
1.5
Red Mulde
1
0.5
0
July
0 0.2 0.4 0.6 0.8 1

esults
2
1.5
1
Rank analysis (different seasons) FTICR/MS
Saubach
2.5
0
Rank 1
Rank 2
Rank 3
Rank 4
0 1
lignins
tannins
0.5
0
March
H/C
O/C
0 0.2 0.4 0.6 0.8 1
2
1.5
Saubach
1
0.5
0
July
0 0.2 0.4 0.6 0.8 1

Question to be answered.
1. What can be said about the relation between
fluorescence and DOC?
2. Is there any seasonal influence on the behaviour?
3. What can be said about the abundance of typical
compound groups?
- seasonal effect
- relation between fluorescence and
biogeochemical pools
4. How can the information be used for using best
raw drinking water?

Flößgrabenquelle
Red Mulde
Black
pool
DOC 0.37 mg/l
DOC 10.3 mg/l
DOC 83 mg/l
2
2
2
1.5
1.5
1.5
H/C
1
H/C
1
H/C
1
0.5
0
0 0.2 0.4 0.6 0.8 1
Pr4 SBA 0809 O/CFlöss Q unten
0.5
0
0 0.2 0.4 0.6 0.8 1
O/C RMU 0809
0.5
0
1:5 diluted
0 0.2 0.4 0.6 0.8 1
O/C
Rote Mulde Pr11
excitation
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
emission
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
undiluted
300 350 400 450 500 550
Emission (nm)

360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
0
200
400
600
800
300 350 400 450 500 550
0
200
400
600
800
0
200
400
600
800
Emission (nm)
300 350 400 450 500 550
0
200
400
600
800
Emission (nm)
300 350 400 450 500 550
Emission (nm)
300 350 400 450 500 550
Emission (nm)
results
EEMF
Comparison between fluorescence ranking
and mass intensity inter samples ranking
Sample EEMF FTICR/MS-Ranks (455 common compounds)
Nr Name Ranks C 17 H 14 O 11 C 20 H 28 O 7
Comp. …
RMU 0709
3
Comp.
455
1 RMU7 1 1 17 … … …
… WMU7 3 2 14 … … …
… TSP7 8 10 16 … … …
… STE7 15 15 19 … … …
… SBA7 18 19 11 … … …
… … … … … … … …
20 … … … … … … …
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
WMU 0709
TSP 0709
STE 0709
SBA 0709
Rank correlation
(Spearman)
FTICR/MS
for each compound

360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
360
340
320
300
280
260
240
0
200
400
600
800
300 350 400 450 500 550
Emission (nm)
0
200
400
600
800
300 350 400 450 500 550
0
200
400
600
800
0
200
400
600
800
Emission (nm)
300 350 400 450 500 550
0
200
400
600
800
Emission (nm)
300 350 400 450 500 550
Emission (nm)
300 350 400 450 500 550
Emission (nm)
results
EEMF
Comparison between fluorescence ranking
and mass intensity inter samples ranking
Sample EEMF FTICR/MS-Ranks (455 common compounds)
Nr Name Ranks C 17 H 14 O 11 C 20 H 28 O 7
Comp. …
RMU 0709
3
Comp.
455
1 RMU7 1 1 17 … … …
… WMU7 3 2 14 … … …
… TSP7 8 10 16 … … …
… STE7 15 15 19 … … …
… SBA7 18 19 11 … … …
… … … … … … … …
20 … … … … … … …
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
Excitation (nm)
WMU 0709
TSP 0709
STE 0709
SBA 0709
Rank correlation
(Spearman)
Level of significance 0.001 non sign.
FTICR/MS
for each compound

esults
H/C
2
1.5
1
0.5
Elemental formulae and their corresponding levels of
xxxsignificance for correlation with humic like fluorescence
Level of significance
for rank correlation
0.001
0.005
0.01
0.025
0.05
0.1
non sig.
C 20 H 28 O 7
C 17 H 14 O 11
0
0 0.2 0.4 0.6 0.8 1
O/C

Answers
Typical compound groups:
- No or minor proteins, carbohydrates, and lipids
- Dominant lignins and tannins
Seasonal behaviour
- Elevated input of tannins during summer months,
mainly in Red Mulde and White Mulde
- Opposite behaviour of the Saubach
Relation of fluorescence – compound groups
- The origin of humic like fluorescence (in this area)
can be allocated within the pool of tannic acids

Question to be answered.
1. What can be said about the relation between
fluorescence and DOC?
2. Is there any seasonal influence on the behaviour?
3. What can be said about the abundance of typical
compound groups?
4. How can the information be used for using best
raw drinking water?

As an example: citation from the U.S. Geolical Survey
tannins are more reactive with chlorine to produce
undesirable disinfection by-products than are terpenoids
the abundance of tannins correlates with humic like
fluorescence
low
Raw water
is appropriate
Fluoresc./DOC
quotient
+
DOC
?
quality
quantity
high
tannins
Raw water
is unsuitable
Drinking water abstraction

4. How can the information be used for using best raw drinking
water?
clear decision
better quality
worse quality
?

Conclusion
• In our study area tannins could be identified as an
ximportant biogeochemical pool within the DOC cycle of
xthe surface water
• The inter samples ranking analysis is the key to connect
xoptical properties of DOM with geochemical pools derived
xfrom FTICR/MS
• The humic like fluorescence is an easy and low cost
xadditional criterion for raw water quality evaluation

Visions:
• Investigation of different fluorescent compounds using
xPARAFAC (statistical fluorescence analysis)
• Correlation of fluorescence compounds derived from
xPARAFAC with geochemical pools derived from van
xKrevelen diagrams
• Correlation of fluorescent compounds and elemental
xformulae compounds with disinfection byproducts
xformation potential or flocculation costs

0.51 1.52 2.50
01
2.5 2,5
2.0 2
1.5 1,5
1.0 1
0.5 0,5
0
0 0,2 0,4 0,6 0,8 1
0,51 1,52 2,50
0
Opportunity to visit me at the posters
NOM characterisation in highly acidic iron rich pore waters of
mine pit lakes using ultra high – resolution mass spectrometry
Peter Herzsprung 1 , Norbert Hertkorn 2 ,MouradHarir 2 , Kurt Friese 1 , Philippe Schmitt-Kopplin 2
1 UFZ Centre for Environmental Research Leipzig-Halle, Department Lake Research, Brückstr. 3a, 39114 Magdeburg, Germany
2 HelmholtzZentrum München, German Research Center for Environment and Health, Institute of Ecological Chemistry, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
Contact: samples & ecology; elemental formula evaluation: peter.herzsprung@ufz.de, FTICR/MS: schmitt-kopplin@helmholtz-muenchen.de
Introduction
Analysis of DOM
The leakage of acidic mine drainage from abandoned mine tailings and High-resolution spectra were acquired (in the negative ion
overburden dumps, as a result of chemical and microbial oxidation of sulphide mode) with a APEX Qe FTICR mass spectrometer (Bruker,
minerals is of major environmental concern. While chemical parameters such Bremen, Germany) equipped with a 12 TESLA superconducting
as the amount of dissolved inorganic ions and the concentration of bulk magnet and an Apollo II microelectrospray source. FTICR mass
dissolved organic carbon (DOC) can be accurately determined, understanding spectral peaks with a signal-to-noise ratio (S/N) > 2 were
of the molecular composition of DOM in such acidic iron-rich environments exported to peak lists. Feasable elemental formulae (C≤100,
remains incomplete. The exceptional complexity of DOM requires highresolution
structural spectroscopy such as electrospray ionisation (ESI) validation). The procedure for extraction of reliable formulae
O≤80, N≤5, S≤1) were computed for each peak (after 13 C
Fourier transform ion cyclotron resonance mass spectrometry (FTICR/MS).
from the data set is described by Herzsrung et al. [1].
Differences in DOM quality abundance of elemental formulae
Lake 111 (2007) Lake Moritzteich (2007)
Lake Waldsee (2007)
Mixolimnion pH 2.6 + Monimolimnion pH 6.8
Monimolimnion pH 6.6
Mixolimnion pH 3.1
Mixolimnion pH 7.0
H/C
0.5
pore-water pH Fe SO 2-
4
DOC
0 - 2.5 cm
Lake 111 2.6 80 mg/l 1060 mg/l 19 mg/l
Lake Moritzteich 6.8 450 mg/l 330 mg/l 220 mg/l
acidic pore water neutral pore water
0
0 0.2 0.4 0.6 0.8 1
O/C
Further differentiation of elemental formulae with common abundance
inter samples ranking analysis of mass intensities
example pH rank within rank within
C 15 H 12 O 9 420 comp. 7 samples
0 – 2 cm 2.8 278 7
2 – 4 cm 3.0 220 5
4 – 6 cm 2.9 221 6
6 – 8 cm 3.0 205 4
10 – 15 cm 3.0 80 3
15 – 20 cm 3.2 71 2
> 20 cm 3.2 35 1
Conclusions
Differences in DOM quality can be demonstrated:
exemplified:
by C x H y O z S 1 and C x H y O z N 1 compounds for different lakes
and 2 different sediment depths respectively
by C x H y O z compounds for 7 sediment depths
by C x H y O z compounds for pore waters from 4 different lakes
• by different abundance diagrams
• by ranking analysis of formulae with
xcommon abundance
• in pore waters from different lakes
• as a function of sediment depth
H/C
2.5
2
C x y x H y O z z
Lake 111 (2009)
0-2 cm
1.5
2-4 cm
4-6 cm
6-8 cm
1
10-15 cm
15-20 cm
0.5
> 20 cm
rank 1
C15H12O9 C 12O 9
0
0 0.2 0.4 0.6 0.8 1
O/C
Trends can be observed:
Lake Waldsee pH Fe SO 2-
4 DOC
pore-water 0 - 2.5 cm 6.7 94 mg/l 68 mg/l 71 mg/l
pore-water > 20 cm 12 < 1 mg/l < 20 mg/l 29 mg/l
2.5
2.5
2.5
C common abundance
common abundance
x H y O z S 1 C x H y O z S 1
C x H y O z N 1
acidic
2
2
2
alkaline
1.5
1.5
1.5
neutral
1
different
1
1
abundance
H/C
0.5
Neutral iron rich pore waters seem to contain
more aromatic and oxygen rich NOM whereas
acidic or even alkaline pore waters are
dominated by more aliphatic and oxygen poor
compounds
H/C
alkaline pore water neutral pore water
0
0 0.2 0.4 0.6 0.8 1
O/C
H/C
2.5
0.5
alkaline pore water neutral pore water
0
0 0.2 0.4 0.6 0.8 1
O/C
Different pore waters
> 20 cm sediment depth (2007)
C x H y O z
References:
common abundance
2
Lake 111
pH 3
1.5
Waldsee
pH 12
1
Moritzteich
pH 6.5
0.5
Lake
rank 1
Goitsche
pH 5.8
0
0 0.2 0.4 0.6 0.8 1
O/C
[1] Herzsprung, P. et al. (2010)
Rapid. Commun. Mass Spectrom. 24,
2909-2924
Photochemical degradation of NOM compounds – itemisation
by single elemental formula evaluation received from ultrahigh-resolution
mass spectrometry
1.5
H/C
1
2.5
0.5
1. Samples from acidic
pit lake pore-waters
Artificial experiment
Sediment
Fe 3+ solution
pore-water
pH 2.3
rich in DOC and Fe 2+
contains no Fe 3+
Sunlight
Artificial solution
2
1.5
H/C
1
Dark
Br control
C x H y O z
0
0 0.2 0.4 0.6 0.8ND
1
O/C
PD
Lake Moritzteich
MinP
pore water (20 – 30 cm)
TD
NewP
2.5
2
0.5
FTICR / MS
Irradiated
sample Q
Comparison of educts and
products formula by formula
Q = Quartz glass
Br = Brown glass
Data mining
C x H y O z S 1
0
0 0.2 0.4 0.6 0.8 1
O/C
Examination of
degradation status
Peter Herzsprung 1a , Wolf von Tümpling 1b , Norbert Hertkorn 2 , Mourad Harir 2 , Kurt Friese 1a , Philippe Schmitt-Kopplin 2
1 UFZ Centre for Environmental Research Leipzig-Halle, Department a Lake Research / b River Ecology, Brückstr. 3a, 39114 Magdeburg, Germany
2 HelmholtzZentrum München, German Research Center for Environment and Health, Institute of Ecological Chemistry, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
Contact: samples & ecology; elemental formula evaluation: peter.herzsprung@ufz.de, ICR-FT/MS: schmitt-kopplin@helmholtz-muenchen.de
Introduction
Analysis of DOM
Sunlight-induced photooxidation is an important removal pathway for DOM. ESI-
High-resolution spectra were acquired (in the negative ion
FTICR/MS as an high end analytical method becomes more and more important
mode) with a APEX Qe FTICR mass spectrometer (Bruker,
to explain sunlight-induced degradation processes [1,2]. For the assessment of
Bremen, Germany) equipped with a 12 TESLA superconducting
magnet and an Apollo II microelectrospray
photochemical changes in DOM a comparison of mass reactants and products,
which were received from FTICR/MS data tables, can be done formula by
source. FTICR mass spectral peaks with a signal-to-noise
formula [1,3]. In a rather simplified experiment, two different types of samples
ratio (S/N) > 2 were exported to peak lists. Feasable
(1. from acidic pit lake pore waters; 2. from a drinking water reservoir tributary)
elemental formulae (C≤100, O≤80, N≤5, S≤1) were computed
were exposed to sunlight. The spectra of the reactants were derived from
for each peak (after 13 C validation). The procedure for
measured dark control (brown glass flask) and the spectra of the products were
extraction of reliable formulae from the data set is
derived from investigated solution in the quartz glass flasks.
described by Herzsrung et al. [3].
Degradation
mass
Sample Calculated
(Da)
ND
ND
NewP
TD
PD
PD
MinP
MinP
H/C ratio
Br
Q
Q
Br
Br
Q
Br
Q
Mass
intensity
206.130680 3487448 18 13 2
206.130680 2700959 18 13 2
207.053159 12215212 9 10 4
207.071785 347281 13 11 1
208.019416 7829639 8 10 3
208.019416 2483020 8 10 3
208.037175 11848488 8 10 5
208.037175 177581290 8 10 5
H
C
O N S
ND: 50% (Br) < Int. (Q) < 200% (Br)
not significantly degraded
PD: Int. (Q) < 50% (Br)
partially degraded
MinP: Int. (Q) > 200% (Br)
minor new photoproduct
TD: Not found in Q, found in Br
totally degraded
NewP: Found in Q, not found in Br
newly formed photoproduct
Allocation of
geochemical pools in
van Krevelen diagrams
Simplified illustration
lipids proteins carbohydrates
lignins
tannins
black carbon
0
0 0.2 0.4 0.6 0.8 1.0
O/C ratio
References:
[1] Gonsior, M. et al. (2009)
Environ. Sci. Technol. 43,
698-703
[2] Kujawinski, E.B. et al. (2004)
Mar. Chem. 92, 23-37
[3] Herzsprung, P. et al. (2010)
Rapid. Commun. Mass Spectrom. 24,
2909-2924
0 0
0 0
1 0
1 1
0 1
0 1
0 0
0 0
2.5
2
1.5
H/C
1
0.5
2. Sample from a drinking
water reservoir tributary
Dark
Br control
Sample
FTICR / MS
Irradiated
sample Q
Comparison of educts and
products formula by formula
C x H y O z
Sunlight
Q = Quartz glass
Br = Brown glass
Data mining
0
0 0.2 0.4 0.6 0.8 1
O/C
Conclusions
Color coded van Krevelen diagrams
differentiation of the photo degradation behaviour of NOM
xxxis possible
Drinking water reservoir tributary
< 10% DOC mineralisation
total degradation mainly of tannic acid like compounds
C x H y O z S 1 not relevant compared to C x H y O z
ND
PD
MinP
TD
NewP
Pore waters from acidic mine pits (+ ferric iron)
> 40% DOC mineralisation on average
degradation of compounds from different geochemical
xxxpools
Higher degradation rate of C x H y O z S 1 compared to C x H y O z

2 0 0 0
1 8 0 0
1 6 0 0
1 4 0 0
1 2 0 0
1 0 0 0
8 0 0
6 0 0
4 0 0
2 0 0
0
2 4 0
2 5 0
2 6 0
2 7 0
2 8 0
2 9 0
3 0 0
3 1 0
3 2 0
3 3 0
3 4 0
3 5 0
3 6 0
Thank you for your attention
2
1.5
1
0.5
0
0 0.2 0.4 0.6 0.8 1
Questions?

Discussion

Discussion
Fluo-Imager M153
Used in the
laboratory
Excitation: 240 – 360 nm
Emission: 260 – 575 nm

Discussion
Excitation (nm)
360
340
320
300
280
irradiated sample
irradiated sample (mean)
0
200
400
600
800
Red Mulde
Excitation (nm)
360
340
320
300
280
dark control
0
200
400
600
800
dark control (mean)
260
9.1 mg/l DOC 260
9.7 mg/l DOC
240
300 350 400 450 500 550
Emission (nm)
dark control - irradiated sample
240
300 350 400 450 500 550
Emission (nm)
dark control - irradiated sample
Excitation (nm)
360
340
320
300
280
0
10
20
30
40
50
Excitation (nm)
360
340
320
300
280
0
50
100
150
200
260
240
300 350 400 450 500 550
Emission (nm)
relative
difference [%]
260
240
300 350 400 450 500 550
Emission (nm)
absolute
difference

Discussion

Discussion
MacDonald, B.C., Lvin, S.J., Patterson, H.: Correction of fluorescence
inner filter effects and the partitioning of pyrene to
dissolved organic carbon. Anal. Chim. Acta 338, 155-162 (1997).

Discussion
Intensity
(humic like fluorescence)
Median values
Excitation:
320 – 345 nm
Emission:
410 – 440 nm
700
600
500
400
300
200
100
0
Humic like fluorescence at different seasons
SBA
RMU8
STE
WMU
WMU7
RMU WMU8
TSP
RMU3
RMU1
SBA1
RMU4
WMU4
0 2 4 6 8 10 12 14
DOC mg/l
RMU7
WMU3
1= January
3= March
4= April
7= July
8= August

Discussion
0,6
RMU7
0,5
WMU3
0,4
RMU3
WMU7
RMU8
SBA
UV254
0,3
STE3
SBA3
TSP3
TSP4
STE
WMU
RMU
TSP
0,2
0,1
SBA8
SBA7
WMU8
TSP7
STE7
RMU4
WMU4
TSP8
STE8
Alle
Linear (Alle)
STE4
SBA4
0
0 2 4 6 8 10 12 14
DOC mg/l

Discussion
700
RMU7
600
RMU8
fluorescence intensity
500
400
300
200
STE4
TSP8
STE8
RMU4
WMU4
SBA7
WMU7
TSP4
TSP7
TSP3
WMU8
STE7 STE3
SBA3
RMU3
WMU3
SBA
STE
WMU
RMU
TSP
Alle
Polynomisch (Alle)
100
SBA4
SBA8
0
0 0,1 0,2 0,3 0,4 0,5 0,6
UV 254nm

Discussion
Evaluation of spectra by PARAFAC
Used: 52 Spectra, 11 sampling sites in 2009
PARAFAC = parallele faktor analysis
• based on a 3D-Matrix
X
( Ix JxK)
I
J
K
with
I … sample
J … excitation wave lengths
K … emission wave lengths
• aim: minimisation of error square sum
• algorithm: alternating least squares (ALS)
result: PARAFAC-model with 5 fluorescent compounds

Discussion
evaluation of spectra by PARAFAC
result: PARAFAC-model with 5 fluorescent compounds
Compound 3
Compound 1
Compound 2
Compound 4 Compound 5

intensity
25
20
15
10
5
0
sample 1 sample 2
sample 3
f 1 f 2 f 3 f 4 f 5
rank value
6
5
4
3
2
1
0
f 1 f 2 f 3 f 4 f 5
scaling by
rank calculation
rank value =
= 1 + number of formulae - rank
rank of
formula 1
formula 2
formula 3
formula 4
formula 5
rank
6
5
4
3
2
1
0
f 1 f 2 f 3 f 4 f 5
Discussion

ank value
10
8
6
4
2
0
sample 1 sample 2 sample 3 sample 4 sample 5
f 1 f 2 f 3 f 4 f 5 f 6 f 7 f 8
Discussion
Rank calculation between
samples considering the
specific rank values for
all formulae which are
abundant in each sample
sample rank compound
1 4 m1=300
2 1 m1=300
3 5 m1=300
4 3 m1=300
5 2 m1=300
rank
6
5
4
3
2
1
0
sample 1 sample 2 sample 3 sample 4 sample 5
f 1 f 2 f 3 f 4 f 5 f 6 f 7 f 8

Discussion
2
Red Mulde / March
2
Red Mulde / April
1.5
1.5
1
1
RMU 0309
RMU 0409
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0
2
1.5
1
0.5
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
Excitation (nm)
360
340
320
300
280
260
240
0
200
400
600
800
RMU 0709
Red Mulde / July
H/C
2,5
0
0
2
Rank 1
Rank 2
1.5
Rank 3
Rank 4
1
0.5
O/C
0 1
260
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
Excitation (nm)
360
340
320
300
280
260
0
200
400
600
800
RMU 0809
Red Mulde / August
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1

Discussion
2
Red Mulde / March
2
Red Mulde / April
1,5
1,5
1
1
0,5
0,5
0
2
1,5
H/C
0 0,2 0,4 0,6 0,8 1
Red Mulde / July
0
O/C
2
1,5
0 0,2 0,4 0,6 0,8 1
Red Mulde / August
1
1
2,5
0,5
0,5
tannins
0
0
Rank 1
Rank 2
0Rank 3 0,2
Rank 4
0,4 0,6 0,8 1
0 1
0
0 0,2 0,4 0,6 0,8 1

Discussion
2
Saubach / March
2
Saubach / April
1.5
1.5
1
SBA 0309
1
SBA 0409
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
260
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
2
Saubach / July
2
Saubach / August
1.5
1.5
1
SBA 0709
1
SBA 0809
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
260
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1

Discussion
2
White Mulde / March
2
White Mulde / April
1.5
1.5
1
1
WMU 0309
WMU 0409
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
260
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
2
White Mulde / July
2
White Mulde / August
1.5
1.5
1
WMU 0709
1
WMU 0809
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
260
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1

Discussion
2
Sauteich Effluent // March
2
Sauteich Effluent // April
1.5
1.5
1
0.5
Excitation (nm)
360
340
Excitation (nm)
320
300
280
360
340
320
300
280
0
200
0
400
200
600
400
800
600
800
TSP 0309
STE 0309
1
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
TSP STE 0409
260
260
260
0
240
300 350 400 450 500 550
240
300 350 400 Emission 450(nm)
500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
2
Sauteich Effluent // July
2
Sauteich Effluent // August
1.5
1.5
1
STE TSP 0709 0709
1
STE TSP 0809 0809
0.5
Excitation (nm)
360
340
320
Excitation (nm)
300
280
360
340
320
300
280
0 0
200 200
400 400
600 600
800 800
0.5
Excitation (nm)
360
340
320
Excitation (nm)
300
280
360
340
320
300
280
0 0
200 200
400 400
600 600
800 800
260
260
260
260
0
240 240
300 300350 350400 400450 450500 500550
550
Emission Emission (nm) (nm)
0
240 240
300 300350 350400 400450 450500 500550
550
Emission Emission (nm) (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1

2
Effluent / March
2
Effluent / April
1.5
1.5
1
1
TSP 0309
TSP 0409
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
260
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
2
Effluent / July
2
Effluent / August
1.5
1.5
1
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
TSP 0709
1
0.5
Excitation (nm)
360
340
320
300
280
0
200
400
600
800
TSP 0809
260
260
0
240
300 350 400 450 500 550
Emission (nm)
0
240
300 350 400 450 500 550
Emission (nm)
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1

2
Discussion
March
Rank 1
2
April
Rank 1
1.5
1.5
H/C
1
H/C
1
Red Mulde
Red Mulde
Saubach
Saubach
0.5
Sauteich
0.5
Sauteich
Effluent
Effluent
White Mulde
White Mulde
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
O/C
O/C
2
2
July
Rank 1
August
Rank 1
1.5
1.5
H/C
1
H/C
1
Red Mulde
Red Mulde
Saubach
Saubach
0.5
Sauteich
0.5
Sauteich
Effluent
Effluent
White Mulde
White Mulde
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
O/C
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
O/C

Discussion
2
Red Mulde / March
2
Red Mulde / April
1.5
1.5
1
1
0.5
0
2
1.5
0 0.2 0.4 0.6 0.8
0
1
Red Mulde / July
2.5
Rank 1
0.5
Rank 2
Rank 3
Rank 04
Rank 5
0 1
2
1.5
0 0.2 0.4 0.6 0.8 1
Red Mulde / August
1
1
0.5
0.5
0
0
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1

Discussion
2,5
Tannic acids
compositions Tanninverbindungen reported in
the aus literature der Literatur
2
CHO
1,5
H/C
1
0,5
no
Tannin 1
Tannin 2
Tannin 3
Tannin 4
0
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
O/C

Discussion
Gallotannins (hydrolyzable)

Discussion
Proanthocyanidins (condensed tannins) polymeric flavanoids

Discussion
Pivot chart
rank RMU
Sample 1 2 3 4 total
RMU3 207 97 97 237 638
RMU4 96 191 228 123 638
RMU7 138 199 195 106 638
RMU8 217 144 115 162 638
total 658 631 635 628 2552

Discussion
0.4
0.3
AImod = (1 + C - 0.5·O - S - 0.5·H) / (C - 0.5·O - S - N)
CHO
SRFA
TSP3
TSP4
TSP7
TSP8
number / total number
0.2
0.1
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
AImod
1000
800
AImod = (1 + C - 0.5·O - S - 0.5·H) / (C - 0.5·O - S - N)
CHO
SRFA
TSP3
TSP4
TSP7
TSP8
total number
600
400
200
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
AImod

0.4
Discussion
AImod = (1 + C - 0.5·O - S - 0.5·H) / (C - 0.5·O - S - N)
0.35
0.3
CHO
RMU3
WMU3
SBA3
STE3
TSP3
0.4
0.35
0.3
AImod = (1 + C - 0.5·O - S - 0.5·H) / (C - 0.5·O - S - N)
CHO
RMU4
WMU4
SBA4
STE4
TSP4
number / total number
0.25
0.2
0.15
number / total number
0.25
0.2
0.15
0.1
0.1
0.05
0.05
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
AImod
AImod
0.4
0.35
0.3
AImod = (1 + C - 0.5·O - S - 0.5·H) / (C - 0.5·O - S - N)
CHO
RMU7
WMU7
SBA7
STE7
TSP7
0.4
0.35
0.3
AImod = (1 + C - 0.5·O - S - 0.5·H) / (C - 0.5·O - S - N)
CHO
RMU8
WMU8
SBA8
STE8
TSP8
number / total number
0.25
0.2
0.15
number / total number
0.25
0.2
0.15
0.1
0.1
0.05
0.05
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
AImod
AImod

2
Discussion
Rank 5
CHO
SRFA C < 21
2
Rank 5
CHO
SRFA C > 20
1.5
Rank 4
1.5
Rank 4
Rank 3
Rank 3
Rank 2
Rank 2
H/C
1
Rank 1
H/C
1
Rank 1
H/C
0.5
Data from
Koch, B.P. et al. (2007)
0
Anal. Chem. 79, 1758
2
1.5
1
0 0.2 0.4 0.6 0.8 1
O/C
Rank 5
Rank 4
Rank 3
Rank 2
Rank 1
CHO
RMU7 C < 21
H/C
0.5
Suwannee River Fulvic Acid
0
2
1.5
1
0 0.2 0.4 0.6 0.8 1
Rank 5
Rank 4
Rank 3
Rank 2
Rank 1
O/C
CHO
RMU7 C > 20
0.5
0
0
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
O/C
O/C
0.5
Red Mulde / July

2.5
2
H/C
1.5
1
0.5
0
CHON1 CHOS1 CHON2-6
0 0.2 0.4 0.6 0.8 1
O/C